Biology subjects

Pramanik, P.

Publications and source records attributed to Pramanik, P..

4 recordsLinked to original sources

bioRxiv2026

Angiotensin II- Angiotensin II Receptor Type 1 Signaling Facilitates Gastric Cancer Metastasis via Kruppel-like Factor 4 Suppression and Tight Junction Breakdown

Cell-Cell adhesion maintained by tight junctions (TJs) is essential for epithelial integrity; loss of TJs correlates with poor prognosis, metastasis, and adverse clinical outcome in gastric cancer (GC). Restoring TJ integrity is therefore considered a promising therapeutic strategy in GC. The study identifies the stomach renin angiotensin system (stRAS) as a crucial regulator of TJ function in GC. Using integrative analysis of GC patient tissues, human GC cell lines, and orthotopic GC xenograft models, here we show that angiotensin II (ATII), the principal effector peptide of stRAS, drives TJ disassembly through an autocrine loop involving angiotensin receptor type 1 (AT1R) expressed on GC cells. Both ATII and AT1R are overexpressed in GC, where they suppress the expression of key TJ proteins. By analyzing global RNA-sequencing (RNA-seq) data and performing CRISPR/Cas9 gene deletion, chromatin immunoprecipitation, and functional assays, we mechanistically reveal that ATII, which is predominantly produced by cancer cells within the tumor microenvironment (TME), inhibits the expression of kruppel-like factor 4 (KLF4), a transcription factor crucial for the transcription of key TJ genes (CLDN1, 3, 4, and TJP1), leading to reduced synthesis of TJ proteins via AT1R expressed on cancer cells. Notably, the study demonstrates the effectiveness of pharmacological inhibition of AT1R with clinically established AT1R antagonists in preventing GC growth and metastasis by restoring TJ stability in vivo. These findings delineate a previously unrecognized role for ATII in governing TJ disassembly in GC and highlight the ATII/AT1R axis as a promising therapeutic target. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/698396v3_ufig1.gif" ALT="Figure 1000"> View larger version (33K): org.highwire.dtl.DTLVardef@a1b9adorg.highwire.dtl.DTLVardef@1a53c5dorg.highwire.dtl.DTLVardef@11e3e1corg.highwire.dtl.DTLVardef@7fd735_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

G4-Attention: Deep Learning Model with Attention for predicting DNA G-Quadruplexes

G-quadruplexes (G4s) are the four-stranded non-canonical nucleic acid secondary structures, formed by the stacking arrangement of the guanine tetramers. They are involved in a wide range of biological roles because of their exceptionally unique and distinct structural characteristics. After the completion of the human genome sequencing project, a lot of bio-informatics algorithms were introduced to predict the active G4s regions in vitro based on the canonical G4 sequence elements, G-richness, and G-skewness, as well as the non-canonical sequence features. Recently, sequencing techniques like G4-seq and G4-ChIP-seq were developed to map the G4s in vitro, and in vivo respectively at a few hundred base resolution. Subsequently, several machine learning and deep learning approaches were developed for predicting the G4 regions using the existing databases. However, their prediction models were simplistic, and the prediction accuracy was notably poor. In response, here, we propose a novel convolutional neural network with Bi-LSTM and attention layers, named G4-Attention, to predict the G4 forming sequences with improved accuracy. G4-Attention achieves high accuracy and attains state-of-the-art results in the G4 propensity and mismatch score prediction task in comparison to other available benchmark models in the literature. Besides the balanced dataset, the developed model can predict the G4 regions accurately in the highly class-imbalanced datasets. Furthermore, the model achieves a significant improvement in the cell-type-specific G4 prediction task. In addition, G4-Attention trained on the human genome dataset can be applied to any non-human genomic DNA sequences to predict the G4 formation propensities accurately. We have also added interpretability analysis of our model to gain further insights. Author summaryG-quadruplex, a non-canonical secondary nucleic acid structure, has emerged as a potential pharmacological target because of its significant implication in several human diseases including cancer, aging, neurological disorders, etc. Despite numerous computational algorithm developments, the prediction of G4 regions accurately in different organisms including humans still remains a challenging task. To address this, in this work, we have presented a novel advanced deep learning architecture called G4-Attention for predicting DNA G-quadruplexes in different organisms including humans. To the best of our knowledge, we are the first to incorporate Bi-LSTM and attention layers on top of a CNN architecture in a deep learning model (G4-Attention) for predicting G4-forming sequences. Our developed model outperforms existing algorithms and achieves current state-of-the-art (SOTA) results in G4 propensity and mismatch score prediction tasks. In addition, the developed model achieves superior results across non-human genomes, class-imbalanced datasets, and cell line-specific datasets. Lastly, G4-Attention can identify key features for understanding the G4 formation mechanism.

bioinformatics↗

bioRxiv2020

Evaluation of Seed or Grain Storage Technologies from Indian Farming Societies Using A Hybrid and Multi-Attribute Approach

Soon after the introduction of green revolution, a parallel increment in percent food grain production and losses has been reported in India, which impedes the mission of achieving food security in India. In parts, among the major causes of these losses are the reluctance among farmers, especially of small holding, towards adoption of scientific storage methods/technologies and inability of the national agencies to meet the challenges imposed by the supply of surplus grains to them by the farmers. Although sets of traditional and improved grain storage technologies exist among Indian farming communities, a controversial account about their effectiveness and performance is available in the literature which negatively impacts the endeavour of bringing rapid developmental in the farming society. In a developing nation like India, a large section of farmers is often either unaware of the modern technologies or cannot logistically access them. They often rely on the traditional or semi-modern technologies without having a comprehensive account of the benefit and economic feasibility of the modern technologies. This impedes the vision of rapid development of the farmers and the process of infusing emerging technologies in the society. In order to develop an informative argument about the performance of the existing technologies, a nearly comprehensive study has been undertaken to compare the different traditional and improved grain storage structures used by Indian farmers. Both qualitative and quantitative parameters were compared individually and simultaneously to achieve their effect on performance individually and in unison. Another objective of the study was to test the applicability of hybrid and multi-attribute approach, based on Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for the performance evaluation and ranking of the attributes and structures. Finally, based on analysis and ideas borrowed out of literature a set of theoretical and conceptual guidelines to assist improvement in these structures was brought in. The study will help understand the status of grain storage technologies in the developing, resource-poor society and also increase the adoption and access of farmers to better post-harvest seed storage technologies.Competing Interest StatementThe authors have declared no competing interest.View Full Text

plant biology↗

bioRxiv2020

On spatial molecular arrangements of SARS-CoV2 genomes of Indian patients

A pandemic caused by the SARS-CoV2 is being experienced by the whole world since December, 2019. A thorough understanding beyond just sequential similarities among the protein coding genes of SARS-CoV2 is important in order to differentiate or relate to the other known CoVs of the same genus. In this study, we compare three genomes namely MT012098 (India-Kerala), MT050493 (India-Kerala), MT358637 (India-Gujrat) from India with NC_045512 (China-Wuhan) to view the spatial as well as molecular arrangements of nucleotide bases of all the genes embedded in these four genomes. Based on different features extracted for each gene embedded in these genomes, corresponding phylogenetic relationships have been built up. Differences in phylogenetic tree arrangement with individual gene suggest that three genomes of Indian origin have come from three different origins or the evolution of viral genome is very fast process. This study would also help to understand the virulence factors, disease pathogenicity, origin and transmission of the SARS-CoV2.

bioinformatics↗